Machine Learning
Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multi-conditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3\% more one-time change addresses than other heuristics.
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This research has produced datasets that can be used openly for other researchers. The dataset is compiled from images of woven fabrics originating from the East Nusa Tenggara Province of Indonesia. A total of 68 fabrics from six districts have been grouped and gone through the image embedding process to become RAW numerical data for further processing. By using Logistic Regression, the classifier accuracy rate for this dataset is only 79.4%. For that other researchers can contribute to improve this accuracy.
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This Matlab code uses Deep GRU and optimized Monarch Butterfly algorithm for MEMS accelerometer temperature drift compensation
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The dataset provided contains the invariants in the form of antecedent and consequent mined using Association Rule Mining with implicit measures such as Confidence, Support, Lift, etc. from an EPIC Plant (Electrical Power and Intelligent Control System).
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This is a dataset for DDoS attack and our dataset has over 50 milion records
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Dataset generated with Unreal Engine 4 and Nvidia NDDS. Contains 1500 images of each object: Forklift, pallet, shipping container, barrel, human, paper box, crate, and fence. These 1500 images are split into 500 images from each environment: HDRI and distractors, HDRI with no distractors, and a randomized environment with distractors.
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We collected several videos about Trump's interviews when he was young and old and the video of the president's swearing in speech on the Internet.
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This work aims to identify anomalous patterns that could be associated with performance degradation and failures in datacenter nodes, such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable early remediation measures, such as Virtual Machines migration and resource reallocation before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization.
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